Calculation of Critical Speed from Raw Training Data in Recreational Marathon Runners

Smyth, Barry; Muniz‐Pumares, Daniel

doi:10.1249/mss.0000000000002412

Cited by 27 publications

(31 citation statements)

References 40 publications

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“…In comparison to supervised studies from Gordon et al ( 2017 ) and Hagan et al ( 1987 ), we observed lower weekly mean values in number of workouts, total training duration, and total distance. However, reduced mean values in training volume have also been shown in other unsupervised investigations (Leyk et al, 2009 ; Smyth and Muniz-Pumares, 2020 ). Lower training volumes might be caused by the heterogeneous nature of the larger data set itself.…”

Section: Discussionsupporting

confidence: 59%

“…Due to this reasons, Hicks et al ( 2019 ) postulated that a plausibility check of the data from portable sensors is an integral part prior to its analysis. Different publications have already shown the potential of portable sensor data from fitness apps to further improve performance prediction (Altini and Amft, 2018 ; Berndsen et al, 2020 ; Emig and Peltonen, 2020 ), to accurately determine the critical speed of runners and to set up pacing strategies (Smyth and Muniz-Pumares, 2020 ) and also to individualize training plans for marathon preparation (Feely et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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Retrospective Analysis of Training and Its Response in Marathon Finishers Based on Fitness App Data

et al. 2021

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Objective: Finishing a marathon requires to prepare for a 42.2 km run. Current literature describes which training characteristics are related to marathon performance. However, which training is most effective in terms of a performance improvement remains unclear.Methods: We conducted a retrospective analysis of training responses during a 16 weeks training period prior to an absolved marathon. The analysis was performed on unsupervised fitness app data (Runtastic) from 6,771 marathon finishers. Differences in training volume and intensity between three response and three marathon performance groups were analyzed. Training response was quantified by the improvement of the velocity of 10 km runs Δv10 between the first and last 4 weeks of the training period. Response and marathon performance groups were classified by the 33.3rd and 66.6th percentile of Δv10 and the marathon performance time, respectively.Results: Subjects allocated in the faster marathon performance group showed systematically higher training volume and higher shares of training at low intensities. Only subjects in the moderate and high response group increased their training velocity continuously along the 16 weeks of training.Conclusion: We demonstrate that a combination of maximized training volumes at low intensities, a continuous increase in average running speed up to the aimed marathon velocity and high intensity runs ≤ 5 % of the overall training volume was accompanied by an improved 10 km performance which likely benefited the marathon performance as well. The study at hand proves that unsupervised workouts recorded with fitness apps can be a valuable data source for future studies in sport science.

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Section: Discussionsupporting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Retrospective Analysis of Training and Its Response in Marathon Finishers Based on Fitness App Data

et al. 2021

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“…Certainly, this model can be improved by incorporating additional sources of data, such as heart-rate data, for example, which may facilitate more accurate judgements about whether a runner has hit the wall. Although such data was not available in our dataset, the increasingly widespread adoption of mobile devices, smartwatches, and wearable sensors [55,56] has the capacity to generate large volumes of additional data (heart-rate, cadence, and power), which may be useful in this regard in the future [57,58]. Already, the availability of such diverse sources of data is enabling several new types of health and fitness applications [59][60][61][62][63] and the emergence of powerful new machine learning techniques has been used to support a variety of related prediction and planning tasks in several sporting domains [64][65][66][67][68][69][70][71][72][73] It is also worth noting that the model of the wall analysed here is defined by a pair of parameters-degree of slowdown and length of slowdown-with specific values-0.25 and 5km, respectively-and it is reasonable to question whether the results would be different if different values had been chosen.…”

Section: Limitationsmentioning

confidence: 99%

How recreational marathon runners hit the wall: A large-scale data analysis of late-race pacing collapse in the marathon

Smyth

2021

PLoS ONE

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Introduction In the marathon, how runners pace and fuel their race can have a major impact on race outcome. The phenomenon known as hitting the wall (HTW) refers to the iconic hazard of the marathon distance, in which runners experience a significant slowing of pace late in the race, typically after the 20-mile mark, and usually because of a depletion of the body’s energy stores. Aim This work investigates the occurrence of significant late-race slowing among recreational marathoners, as a proxy for runners hitting the wall, to better understand the likelihood and nature of such slowdowns, and their effect on race performance. Methods Using pacing data from more than 4 million race records, we develop a pacing-based definition of hitting the wall, by identifying runners who experience a sustained period of slowing during the latter stages of the marathon. We calculate the cost of these slowdowns relative to estimates of the recent personal-best times of runners and compare slowdowns according to runner sex, age, and ability. Results We find male runners more likely to slow significantly (hit the wall) than female runners; 28% of male runners hit the wall compared with 17% of female runners, χ2(1, N = 1, 928, 813) = 27, 693.35, p < 0.01, OR = 1.43. Such slowdowns are more frequent in the 3 years immediately before and after a recent personal-best (PB) time; for example, 36% of all runners hit the wall in the 3 years before a recent PB compared with just 23% in earlier years, χ2(1, N = 509, 444) = 8, 120.74, p < 0.01, OR = 1.31. When runners hit the wall, males slow more than females: a relative slowdown of 0.40 vs. 0.37 is noted, for male and female runners, when comparing their pace when they hit the wall to their earlier race (5km-20km) pace, with t(475, 199) = 60.19, p < 0.01, d = 0.15. And male runners slow over longer distances than female runners: 10.7km vs. 9.6km, respectively, t(475, 199) = 68.44, p < 0.01, d = 0.17. Although, notably the effect size of these differences is small. We also find the finish-time costs of hitting the wall (lost minutes) to increase with ability; r2(7) = 0.91, p < 0.01 r2(7) = 0.81, p < 0.01 for male and female runners, respectively. Conclusions While the findings from this study are consistent with qualitative results from earlier single-race or smaller-scale studies, the new insights into the risk and nature of slowdowns, based on the runner sex, age, and ability, have the potential to help runners and coaches to better understand and calibrate the risk/reward trade-offs that exist as they plan for future races.

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“…Besides, CS can be estimated using personal best times, which does not require the participant to go to the laboratory (Jones et al, 2019). Finally, a recent study demonstrated that using estimations of CS from raw training data can be sufficient to successfully predict marathon performance and provide useful pacing information (Smyth and Muniz-Pumares, 2020).…”

Section: Discussionmentioning

confidence: 99%

Effect of Mathematical Modeling and Fitting Procedures on the Assessment of Critical Speed and Its Relationship With Aerobic Fitness Parameters

et al. 2021

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An accurate estimation of critical speed (CS) is important to accurately define the boundary between heavy and severe intensity domains when prescribing exercise. Hence, our aim was to compare CS estimates obtained by statistically appropriate fitting procedures, i.e., regression analyses that correctly consider the dependent variables of the underlying models. A second aim was to determine the correlations between estimated CS and aerobic fitness parameters, i.e., ventilatory threshold, respiratory compensation point, and maximal rate of oxygen uptake. Sixteen male runners performed a maximal incremental aerobic test and four exhaustive runs at 90, 100, 110, and 120% of the peak speed of the incremental test on a treadmill. Then, two mathematically equivalent formulations (time as function of running speed and distance as function of running speed) of three different mathematical models (two-parameter, three-parameter, and three-parameter exponential) were employed to estimate CS, the distance that can be run above CS (d′), and if applicable, the maximal instantaneous running speed (smax). A significant effect of the mathematical model was observed when estimating CS, d′, and smax (P < 0.001), but there was no effect of the fitting procedure (P > 0.77). The three-parameter model had the best fit quality (smallest Akaike information criterion) of the CS estimates but the highest 90% confidence intervals and combined standard error of estimates (%SEE). The 90% CI and %SEE were similar when comparing the two fitting procedures for a given model. High and very high correlations were obtained between CS and aerobic fitness parameters for the three different models (r ≥ 0.77) as well as reasonably small SEE (SEE ≤ 6.8%). However, our results showed no further support for selecting the best mathematical model to estimate critical speed. Nonetheless, we suggest coaches choosing a mathematical model beforehand to define intensity domains and maintaining it over the running seasons.

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Calculation of Critical Speed from Raw Training Data in Recreational Marathon Runners

Abstract: Supplemental digital content is available in the text.

Cited by 27 publications

References 40 publications

Retrospective Analysis of Training and Its Response in Marathon Finishers Based on Fitness App Data

Retrospective Analysis of Training and Its Response in Marathon Finishers Based on Fitness App Data

How recreational marathon runners hit the wall: A large-scale data analysis of late-race pacing collapse in the marathon

Effect of Mathematical Modeling and Fitting Procedures on the Assessment of Critical Speed and Its Relationship With Aerobic Fitness Parameters

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